1. Field of the Invention
The present invention relates to home appliance classification based on power consumption, and more particularly, to an apparatus and method for classifying home appliances based on power consumption using deep learning, where home appliances in use may be efficiently classified by applying deep learning to analyze power data collected from a customer's house.
2. Discussion of Related Art
In recent years, globally, there has been increased interest in efficient use of energy because of the depletion of fossil fuels. Accordingly, a smart grid technology that combines ICT technology with an electrical grid has been realized, mainly in developed countries such as the US and Europe.
A smart meter that measures and transmits power consumption in each customer's house is installed to enable monitoring and power usage prediction based on the power consumption.
However, it is difficult to predict energy usage accurately by only collecting total power consumption of a customer's house. In order to more accurately make predictions, it is necessary to collect power consumption information at the stage of the home appliances used in the customer's house.
As a technology for realizing this, non-intrusive Load Monitoring (NILM) is provided.
NILM is a technology for analyzing power data of a customer's house to deduce what appliances are being used. Accordingly, the collection of power consumption information is allowed at the stage of the home appliances by using NILM.
However, home appliance classification models introduced by many studies conducted so far have accuracy that is too low to be commercialized.
Most studies on the power consumption model in NILM have used a factorial hidden Markov model (FHMM), a conditional FHMM (CFHMM), a Hierarchical FHMM (HieFHMM), etc., which are based on a hidden Markov model (HMM).
However, the HMM is a model based on probability of going from the current state to the next state, and thus the conventional models based thereof have two common problems.
First, as the number of home appliances increases, complexity of the models increases, and thus classification accuracy decreases.
Second, the HMM can perform accurate modeling when a home appliance operates with single-state power consumption. However, when a home appliance that operates with multi-state power consumption is modeled, the classification accuracy decreases.
Accordingly, there is a need to develop a new home appliance classification method that can solve the problems of the conventional HMM-based home appliance classification models in order to increase accuracy.